Эффекты психологических интервенций на ассоциированные с математической тревожностью функциональные связи головного мозга
Ключевые слова:
математическая тревожность, психологические интервенции, ЭЭГ, функциональные связи, функциональные сети
Аннотация
Обоснование. Математическая тревожность связана с негативными эмоциональными переживаниями, возникающими при необходимости решать математические задачи. Высокий уровень математической тревожности снижает вероятность выбора образовательного и карьерного трека в STEM (наука, технология, инженерное дело и математика) дисциплинах. Для России за последние годы проблема снижения числа студентов STEM-направлений приняла особую остроту. Поэтому изучение методов снижения/регуляции математической тревожности приобретает высокую актуальность. Цель: провести анализ воздействия трёх типов однократных психологических интервенций – экспрессивного письма, переоценки установки отношения к математике и релаксации, с точки зрения способности регулировать объективные нейрофизиологические корреляты математической тревожности. Материалы и методы. Выборка обследованных (78 студентов вузов) была разделена наодну контрольную и три экспериментальные группы. Работа основана на использовании опросника для измерения уровня математической тревожности и объективных электроэнцефалографических данных. Анализ данных был выполнен на основе двухэтапной методологии выделения функциональных связей и функциональных сетей головного мозга. Результаты. Обнаружены отрицательные корреляции (на уровне тенденции) ( ) между уровнем математической тревожности и показателями обменной эффективности функциональных сетей альфа-1 (8–10 Гц) и бета-1 (13–20 Гц) диапазонов. Значимых эффектов психологических интервенций выявлено не было. Заключение. Более эффективная организация функциональных сетей головного мозга ассоциирована с меньшим уровнем математической тревожности. Отсутствие эффектов однократных интервенций свидетельствует о необходимости изучения различных по продолжительности реализации программ регуляции уровня математической тревожности.
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Литература
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10. Crum A.J., Jamieson J.P., Akinola M. Optimizing stress: An integrated intervention for regulating stress responses. Emotion. 2020;20(1):120–125. DOI: https://doi.org/10.1037/emo0000670
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23. Schoonhoven D.N., Briels C.T., Hillebrand A. et al. Sensitive and reproducible MEG resting-state metrics of functional connectivity in Alzheimers disease. Alzheimers research and therapy. 2022;14(1):38. DOI: https://doi.org/10.1186/s13195-022-00970-4
24. Ismail L.E., Karwowski W. A graph theory-based modeling of functional brain connectivity based on EEG: A systematic review in the context of neuroergonomics. IEEE Access. 2020;8:155103–155135.
25. Watts D.J., Strogatz S.H. Collective dynamics of small-worldnetworks. Nature. 1998;393(6684):440–442.
26. Bassett D.S., Sporns O. Network neuroscience. Nature neuroscience. 2017;20(3):353–364. DOI: https://doi.org/10.1038/nn.4502
27. Latora V., Marchiori M. Efficient behavior of small-world networks. Physical review letters. 2001;87(19):198701. DOI: https://doi.org/10.1103/PhysRevLett.87.198701
28. Greicius M.D., Krasnow B., Reiss A.L., Menon V. Functional connectivity in the resting brain: a network analysis of the default mode hypothesis. Proceedings of the National Academy of Sciences. 2003;100(1):253–258. DOI: https://doi.org/10.1073/pnas.0135058100
29. Artemenko C., Daroczy G., Nuerk H.C. Neural correlates of math anxiety – an overview and implications. Frontiers in Psychology. 2015;6:1333. DOI: https://doi.org/10.3389/fpsyg.2015.01333
30. Klados M.A., Pandria N., Micheloyannis S. et al. Math anxiety: Brain cortical network changes in anticipation of doing mathematics. International Journal of Psychophysiology. 2017;122:24–31. DOI: https://doi.org/10.1016/j.ijpsycho.2017.05.003
31. Klados M.A., Paraskevopoulos E., Pandria N., Bamidis P.D. The impact of math anxiety on working memory: A cortical activations and cortical functional connectivity EEG study. IEEE Access. 2019;7:15027–15039.
32. Esipenko E.A., Matsepuro D.M., Arhipova O.V. et al. Physiological correlates of mathematical anxiety in resting state and during anticipation of math. Psikhologiya. Psikhofiziologiya = Psychology. Psychophysiology. 2022;15(1):131–141. DOI: https://doi.org/10.14529/jpps220112.
33. Savostyanov A. N. et al. EEG Correlates of Trait and Mathematical Anxiety during Lexical and Numerical Error-Recognition Tasks. International Journal of Social, Behavioral, Educational, Economic and Management Engineering. 2015;9(7):2162–2166.
34. Hopko D.R., Mahadevan R., Bare R.L., Hunt M.K. The Abbreviated Math Anxiety Scale (AMAS): Construction, Validity, and Reliability. Assessment. 2003;10(2):178–182. DOI: https://doi.org/10.1177/1073191103010002008
35. Jamieson J.P., Mendes W.B., Blackstock E., Schmader T. Turning the knots in your stom-ach into bows: Reappraising arousal improves performance on the GRE. Journal of Experimental Social Psychology. 2010;46(1):208–212. DOI: https://doi.org/10.1016/j.jesp.2009.08.015
36. Brunyé T.T., Mahoney C.R., Giles G.E. et. al. Learning to relax: Evaluating four brief in-terventions for overcoming the negative emotions accompanying math anxiety. Learning and In-dividual Differences. 2013;27(7):1–7. DOI: https://doi.org/10.1016/j.lindif.2013.06.008
37. Peng C.Y.J., Chen L.T. Beyond Cohens d: Alternative effect size measures for between-subject designs. The Journal of Experimental Education. 2014;82(1):22–50. DOI: https://doi.org/10.1080/ 00220973.2012.745471
38. Chaddock-Heyman L. Weng T.B., Kienzler C. et al. Scholastic performance and functional connectivity of brain networks in children. PloS one. 2018;13(1):e0190073. DOI: https://doi.org/10.1371/journal.pone.0190073
39. Ramirez G., Shaw S.T., Maloney E.A. Math anxiety: Past research, promising interventions, and a new interpretation framework. Educational Psychologist. 2018;53(3):145–164. DOI: https://doi.org/10.1080/00461520.2018.1447384
40. Lyons I.M., Beilock S.L. When math hurts: math anxiety predicts pain network activation in anticipation of doing math. PloS one. 2012;7(10):e48076. DOI: https://doi.org/10.1371/journal.pone.0048076
41. Xie W., Toll R.T., Nelson C.A. EEG Functional Connectivity Analysis in the Source Space. Developmental Cognitive Neuroscience. 2022:101119. DOI: https://doi.org/10.1016/j.dcn.2022.101119
2. Whyte J., Anthony G. Maths anxiety: The fear factor in the mathematics classroom. New Zealand Journal of Teachers Work. 2012;9(1):6–15.
3. Chang H., Beilock S.L. The math anxiety-math performance link and its relation to individ-ual and environmental factors: A review of current behavioral and psychophysiological research. Current Opinion in Behavioral Sciences. 2016;10;33–38. DOI: https://doi.org/10.1016/j.cobeha.2016.04.011
4. Chemekov V.N., Krylov D.A. STEAM-a new approach to engineering education. Vestnik Mariiskogo gosudarstvennogo universiteta = Bulletin of the Mari State University. 2015;5(20):59–64.
5. Moustafa A.A., Al-Emadi A.A., Megreya A.M. The Need to Develop an Individualized Intervention for Mathematics Anxiety. Frontiers in Psychology. 2021:12. DOI: https://doi.org/10.3389/ fpsyg.2021.723289
6. Samuel T.S., Warner J. “I can math!”: Reducing math anxiety and increasing math self-efficacy using a mindfulness and growth mindset-based intervention in first-year students. Com-munity College Journal of Research and Practice. 2021;45(3):205–222. DOI: https://doi.org/10.1080/ 10668926.2019.1666063
7. Henslee A., Klein B. Using brief guided imagery to reduce math anxiety and improve math performance: A pilot study. Journal of STEM Education. 2017;18(4):32–36.
8. Durak Y.H. The effects of using different tools in programming teaching of secondary school students on engagement, computational thinking and reflective thinking skills for problem solving. Technology, Knowledge and Learning. 2020;25(1):179–195. DOI: https://doi.org/10.1007/s10758-018-9391-y
9. Park D., Ramirez G., Beilock S.L. The Role of Expressive Writing in Math Anxiety. Journal of Experimental Psychology: Applied. 2014;20(2):103–111. DOI: https://doi.org/10.1037/xap0000013
10. Crum A.J., Jamieson J.P., Akinola M. Optimizing stress: An integrated intervention for regulating stress responses. Emotion. 2020;20(1):120–125. DOI: https://doi.org/10.1037/emo0000670
11. Chen M. Research on Math Anxiety of Elementary School Teachers. In 2021. International Conference on Education, Language and Art (ICELA 2021). 2022;637:325–330.
12. Power J.D., Cohen A.L., Nelson S.M. et al. Functional network organization of the human brain. Neuron. 2011;72(4):665–678. DOI: https://doi.org/10.1016/j.neuron.2011.09.006
13. Rubinov M., Sporns O. Complex network measures of brain connectivity: uses and interpretations. Neuroimage. 2010;52(3):1059–1069. DOI: https://doi.org/10.1016/j.neuroimage.2009.10.003
14. Bastos A.M., Schoffelen J.M. A tutorial review of functional connectivity analysis methods and their interpretational pitfalls. Frontiers in systems neuroscience. 2016;9:175. DOI: https://doi.org/ 10.3389/fnsys.2015.00175
15. Colclough G.L., Brookes M.J., Smith S.M., Woolrich M.W. A symmetric multivariate leakage correction for MEG connectomes. Neuroimage. 2015;117:439–448. DOI: https://doi.org/10.1016/j.neuroimage.2015.03.071
16. Stam C.J., Nolte G., Daffertshofer A. Phase lag index: assessment of functional connec-tivity from multi-channel EEG and MEG with diminished bias from common sources. Human brain mapping. 2007;28(11):1178–1193. DOI: https://doi.org/10.1002/hbm.20346
17. Nolte G., Bai O., Wheaton L. et al. Identifying true brain interaction from EEG data using the imaginary part of coherency. Clinical Neurophysiology. 2004;115(10):2292–2307. DOI: https://doi.org/10.1016/j.clinph.2004.04.029
18. Colclough G.L., Woolrich M.W., Tewarie P.K. et al. How reliable are MEG resting-state connectivity metrics? Neuroimage. 2016;138:284–293. DOI: https://doi.org/10.1016/j.neuroimage.2016.05.070
19. van den Heuvel M.P., Stam C.J., Kahn R.S., Pol H.E.H. Efficiency of functional brain networks and intellectual performance. Journal of Neuroscience. 2009;29(23):7619–7624. DOI: https://doi.org/ 10.1523/JNEUROSCI.1443-09.2009
20. Modi S., Kumar M., Kumar P., Khushu S. Aberrant functional connectivity of resting state networks associated with trait anxiety. Psychiatry Research: Neuroimaging. 2015;234(1):25–34. DOI: https://doi.org/10.1016/j.pscychresns.2015.07.006
21. Brookes M.J., Woolrich M.W., Barnes G.R. Measuring functional connectivity in MEG: a multivariate approach insensitive to linear source leakage. Neuroimage. 2012;63(2):910–920. DOI: https://doi.org/10.1016/j.neuroimage.2012.03.048
22. Hardmeier M., Hatz F., Bousleiman H. et al. Reproducibility of functional connectivity and graph measures based on the phase lag index (PLI) and weighted phase lag index (wPLI) derived from high resolution EEG. PloS one. 2014;9(10):e108648. DOI: https://doi.org/10.1371/journal.pone.0108648
23. Schoonhoven D.N., Briels C.T., Hillebrand A. et al. Sensitive and reproducible MEG resting-state metrics of functional connectivity in Alzheimers disease. Alzheimers research and therapy. 2022;14(1):38. DOI: https://doi.org/10.1186/s13195-022-00970-4
24. Ismail L.E., Karwowski W. A graph theory-based modeling of functional brain connectivity based on EEG: A systematic review in the context of neuroergonomics. IEEE Access. 2020;8:155103–155135.
25. Watts D.J., Strogatz S.H. Collective dynamics of small-worldnetworks. Nature. 1998;393(6684):440–442.
26. Bassett D.S., Sporns O. Network neuroscience. Nature neuroscience. 2017;20(3):353–364. DOI: https://doi.org/10.1038/nn.4502
27. Latora V., Marchiori M. Efficient behavior of small-world networks. Physical review letters. 2001;87(19):198701. DOI: https://doi.org/10.1103/PhysRevLett.87.198701
28. Greicius M.D., Krasnow B., Reiss A.L., Menon V. Functional connectivity in the resting brain: a network analysis of the default mode hypothesis. Proceedings of the National Academy of Sciences. 2003;100(1):253–258. DOI: https://doi.org/10.1073/pnas.0135058100
29. Artemenko C., Daroczy G., Nuerk H.C. Neural correlates of math anxiety – an overview and implications. Frontiers in Psychology. 2015;6:1333. DOI: https://doi.org/10.3389/fpsyg.2015.01333
30. Klados M.A., Pandria N., Micheloyannis S. et al. Math anxiety: Brain cortical network changes in anticipation of doing mathematics. International Journal of Psychophysiology. 2017;122:24–31. DOI: https://doi.org/10.1016/j.ijpsycho.2017.05.003
31. Klados M.A., Paraskevopoulos E., Pandria N., Bamidis P.D. The impact of math anxiety on working memory: A cortical activations and cortical functional connectivity EEG study. IEEE Access. 2019;7:15027–15039.
32. Esipenko E.A., Matsepuro D.M., Arhipova O.V. et al. Physiological correlates of mathematical anxiety in resting state and during anticipation of math. Psikhologiya. Psikhofiziologiya = Psychology. Psychophysiology. 2022;15(1):131–141. DOI: https://doi.org/10.14529/jpps220112.
33. Savostyanov A. N. et al. EEG Correlates of Trait and Mathematical Anxiety during Lexical and Numerical Error-Recognition Tasks. International Journal of Social, Behavioral, Educational, Economic and Management Engineering. 2015;9(7):2162–2166.
34. Hopko D.R., Mahadevan R., Bare R.L., Hunt M.K. The Abbreviated Math Anxiety Scale (AMAS): Construction, Validity, and Reliability. Assessment. 2003;10(2):178–182. DOI: https://doi.org/10.1177/1073191103010002008
35. Jamieson J.P., Mendes W.B., Blackstock E., Schmader T. Turning the knots in your stom-ach into bows: Reappraising arousal improves performance on the GRE. Journal of Experimental Social Psychology. 2010;46(1):208–212. DOI: https://doi.org/10.1016/j.jesp.2009.08.015
36. Brunyé T.T., Mahoney C.R., Giles G.E. et. al. Learning to relax: Evaluating four brief in-terventions for overcoming the negative emotions accompanying math anxiety. Learning and In-dividual Differences. 2013;27(7):1–7. DOI: https://doi.org/10.1016/j.lindif.2013.06.008
37. Peng C.Y.J., Chen L.T. Beyond Cohens d: Alternative effect size measures for between-subject designs. The Journal of Experimental Education. 2014;82(1):22–50. DOI: https://doi.org/10.1080/ 00220973.2012.745471
38. Chaddock-Heyman L. Weng T.B., Kienzler C. et al. Scholastic performance and functional connectivity of brain networks in children. PloS one. 2018;13(1):e0190073. DOI: https://doi.org/10.1371/journal.pone.0190073
39. Ramirez G., Shaw S.T., Maloney E.A. Math anxiety: Past research, promising interventions, and a new interpretation framework. Educational Psychologist. 2018;53(3):145–164. DOI: https://doi.org/10.1080/00461520.2018.1447384
40. Lyons I.M., Beilock S.L. When math hurts: math anxiety predicts pain network activation in anticipation of doing math. PloS one. 2012;7(10):e48076. DOI: https://doi.org/10.1371/journal.pone.0048076
41. Xie W., Toll R.T., Nelson C.A. EEG Functional Connectivity Analysis in the Source Space. Developmental Cognitive Neuroscience. 2022:101119. DOI: https://doi.org/10.1016/j.dcn.2022.101119
References
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Опубликован
2023-06-30
Как цитировать
Яковлев, Н., Есипенко, Е., Архипова, О., Марейчева, Е., Маташова, Т., & Мацепуро, Д. (2023). Эффекты психологических интервенций на ассоциированные с математической тревожностью функциональные связи головного мозга. Психология. Психофизиология, 16(2), 93-103. https://doi.org/10.14529/jpps230209
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Психофизиология
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